code: def predict(self, interm_res, timeout):
      self.interm_res = interm_res
      self.step = len(interm_res)
      func_params = self.fit_theta()
      filtered_funcs = self.filter_curve(func_params)
      final_keys = set(func_params.keys()) & set(filtered_funcs.keys())
      filtered_funcs = {
          func_name: filtered_funcs[func_name]
          for func_name in final_keys
      }
      func_params = {
          func_name: func_params[func_name] for func_name in final_keys
      }
      if len(filtered_funcs) < CurveModel.LEAST_FITTED_FUNCTION:
          return None
      weights_samples = self.mcmc_sampling(
          filtered_funcs, func_params, timeout
      )
      f_res = [
          self.f_comb(
              self.iterations,
              weight_sample,
              filtered_funcs,
              func_params,
          )
          for weight_sample in weights_samples
      ]
      return np.mean(f_res)
  
imports: import scipy
  import logging
  import numpy
  import time
  from scipy import optimize
label_desc: Use image node_id 12 for calling the CurveModel obj's underlying member method code with example usage: obj.predict(interm_res, timeout) and returns: np, None